Is keeping data science productive becoming an uphill struggle?

Your team has the skills – business knowledge, statistical versatility, programming, modeling, and visual analysis – to unlock the insight you need. But you can’t connect the dots if they can’t connect reliably with the data they need.

New ideas don’t fit old data models

Friction vs. innovation

Experimentation can be messy, but out-of-the-box exploration needs to preserve the autonomy of data scientists

THE SOLUTION

At ElevationData, we think there’s a better way

The Data Science Pipeline by ElevationData gives you faster, more productive automation and orchestration across a broad range of advanced dynamic analytic workloads. It helps you engineer production-grade services using a portfolio of proven cloud technologies to move data across your system.

Built from the leading AWS technologies for ingest, streaming, storage, microservices, and processing technologies, it gives you the versatility to experiment across data sets, from early phase exploration to machine learning models. You get a data infrastructure ideally suited for unique demands of access, processing, and consumption throughout the data science and analytic lifecycle.

Agile Analytics

How We Do It

Data-science projects can go sideways when they get in over their head on data engineering and infrastructure tasks. They get mired with a Frankenstein cloud that undermines repeatability and iteration.

We’ve solved for that with a generalizable, production-grade data pipeline architecture; it’s well-suited to the iteration and customization typical of advanced analytics workloads and data flows. Tghat provides much more direct path for achieving real results that are both reliable and scalable.

Alex Ulyanov CTO, ElevationData

Scale-out Data Lake for Data Science

End-to-end Analytics Processing

RedShift

Fast, scalable, simple, and cost-effective way to analyze data across data warehouses/data lakes